Sociodemographic Factors are Associated with Care Delivery and Outcomes in Pediatric Severe Sepsis.

IMPORTANCE: Sepsis is a leading cause of morbidity and mortality in the United States and disparate outcomes exist between racial/ethnic groups despite improvements in sepsis management. These observed differences are often related to social determinants of health (SDoH). Little is known about the role of SDoH on outcomes in pediatric sepsis. OBJECTIVE: This study examined the differences in care delivery and outcomes in children with severe sepsis based on race/ethnicity and neighborhood context (as measured by the social vulnerability index). DESIGN SETTING AND PARTICIPANTS: This retrospective, cross-sectional study was completed in a quaternary care children's hospital. Patients 18 years old or younger who were admitted between May 1, 2018, and February 28, 2022, met the improving pediatric sepsis outcomes (IPSO) collaborative definition for severe sepsis. Composite measures of social vulnerability, care delivery, and clinical outcomes were stratified by race/ethnicity. MAIN OUTCOMES AND MEASURES: The primary outcome of interest was admission to the PICU. Secondary outcomes were sepsis recognition and early goal-directed therapy (EGDT). RESULTS: A total of 967 children met the criteria for IPSO-defined severe sepsis, of whom 53.4% were White/non-Hispanic. Nearly half of the cohort (48.7%) required PICU admission. There was no difference in illness severity at PICU admission by race (1.01 vs. 1.1, p = 0.18). Non-White race/Hispanic ethnicity was independently associated with PICU admission (odds ratio [OR] 1.35 [1.01-1.8], p = 0.04). Although social vulnerability was not independently associated with PICU admission (OR 0.95 [0.59-1.53], p = 0.83), non-White children were significantly more likely to reside in vulnerable neighborhoods (0.66 vs. 0.38, p < 0.001). Non-White race was associated with lower sepsis recognition (87.8% vs. 93.6%, p = 0.002) and less EGDT compliance (35.7% vs. 42.8%, p = 0.024). CONCLUSIONS AND RELEVANCE: Non-White race/ethnicity was independently associated with PICU admission. Differences in care delivery were also identified. Prospective studies are needed to further investigate these findings.

The association between food access and frailty among older adults with gastrointestinal malignancies-The CARE Registry.

BACKGROUND: Food access is associated with higher gastrointestinal (GI) cancer mortality; however, its association with frailty, which is a predictor of premature mortality among older adults with cancer, is less understood. METHODS: The authors included 880 adults aged 60 years and older who were recently diagnosed with GI cancers and were undergoing self-reported geriatric assessment at their first prechemotherapy visit to the University of Alabama at Birmingham oncology clinic. Food access was measured using the 2019 US Department of Agriculture Economic Research Service designation low-income, low-access (LILA), classifying census tracts based on income and/or access to food stores at various distances. The primary outcome was frailty on the CARE (Cancer and Aging Resilience Evaluation) Frailty Index, a composite of the proportion of impaired geriatric assessment measures. The authors examined the LILA-frailty association with modified Poisson regression accounting for census-tract clustering. RESULTS: The median patient age was 69 years, 58.1% were men, 22.5% were non-Hispanic Black, 29.2% had colorectal cancer, 28.0% had pancreatic cancer, 70.1% presented with stage III/IV disease, and 34.9% were frail. A higher proportion in LILA areas were non-Hispanic Black (44.1% vs. 10.8%; p < .001) and had less education (high school or less: 48.1% vs. 37.9%; p = .020). Adjusting for age, race and ethnicity, sex, cancer type and stage, and education, an LILA designation was associated with 58% greater odds of worsening frailty status (95% confidence interval, 1.18-2.12). An analysis of LILA subcategories revealed that associations were maintained across all LILA measures. CONCLUSIONS: Poor food access was associated with a greater risk of frailty among newly diagnosed older adults with GI cancers before they received systemic treatment. Intervening on local food access, particularly in LILA areas, may be a target for improving rates of frailty and promoting health equity in this population.

Association of Neighborhood-Level Environmental Injustice With Health Status in the US.

This cross-sectional study uses the Environmental Justice Index to assess the association between environmental injustice and health status at the neighborhood level.

Development and implementation of a distributed data network between an academic institution and state health departments to investigate variation in time to HIV viral suppression in the Deep South.

BACKGROUND: Achieving early and sustained viral suppression (VS) following diagnosis of HIV infection is critical to improving outcomes for persons with HIV (PWH). The Deep South of the United States (US) is a region that is disproportionately impacted by the domestic HIV epidemic. Time to VS, defined as time from diagnosis to initial VS, is substantially longer in the South than other regions of the US. We describe the development and implementation of a distributed data network between an academic institution and state health departments to investigate variation in time to VS in the Deep South. METHODS: Representatives of state health departments, the Centers for Disease Control and Prevention (CDC), and the academic partner met to establish core objectives and procedures at the beginning of the project. Importantly, this project used the CDC-developed Enhanced HIV/AIDS Reporting System (eHARS) through a distributed data network model that maintained the confidentiality and integrity of the data. Software programs to build datasets and calculate time to VS were written by the academic partner and shared with each public health partner. To develop spatial elements of the eHARS data, health departments geocoded residential addresses of each newly diagnosed individual in eHARS between 2012-2019, supported by the academic partner. Health departments conducted all analyses within their own systems. Aggregate results were combined across states using meta-analysis techniques. Additionally, we created a synthetic eHARS data set for code development and testing. RESULTS: The collaborative structure and distributed data network have allowed us to refine the study questions and analytic plans to conduct investigations into variation in time to VS for both research and public health practice. Additionally, a synthetic eHARS data set has been created and is publicly available for researchers and public health practitioners. CONCLUSIONS: These efforts have leveraged the practice expertise and surveillance data within state health departments and the analytic and methodologic expertise of the academic partner. This study could serve as an illustrative example of effective collaboration between academic institutions and public health agencies and provides resources to facilitate future use of the US HIV surveillance system for research and public health practice.

Geospatial Analysis of Time to Human Immunodeficiency Virus (HIV) Diagnosis and Adult HIV Testing Coverage Highlights Areas for Intervention in the US Southeast.

Background: In the United States (US), 44% of people with human immunodeficiency virus (PWH) live in the Southeastern census region; many PWH remain undiagnosed. Novel strategies to inform testing outreach in rural states with dispersed HIV epidemics are needed. Methods: Alabama state public health HIV testing surveillance data from 2013 to 2017 were used to estimate time from infection to HIV diagnosis using CD4 T-cell depletion modeling, mapped to county. Diagnostic HIV tests performed during 2013-2021 by commercial testing entities were used to estimate HIV tests per 100 000 adults (aged 15-65 years), mapped to client ZIP Code Tabulation Area (ZCTA). We then defined testing "cold spots": those with <10% adults tested plus either (1) within or bordering 1 of the 13 counties with HIV prevalence >400 cases per 100 000 population or (2) within a county with average time to diagnosis greater than the state average to inform testing outreach. Results: Time to HIV diagnosis was a median of 3.7 (interquartile range [IQR], 0-9.2) years across Alabama, with a range of 0.06-12.25 years. Approximately 63% of counties (n = 42) had a longer time to diagnosis compared to national US estimates. Six hundred forty-three ZCTAs tested 17.3% (IQR, 10.3%-25.0%) of the adult population from 2013 to 2017. To prioritize areas for testing outreach, we generated maps to describe 47 areas of HIV-testing cold spots at the ZCTA level. Conclusions: Combining public health surveillance with commercial testing data provides a more nuanced understanding of HIV testing gaps in a state with a rural HIV epidemic and identifies areas to prioritize for testing outreach.

Geographic disparities in peripartum cardiomyopathy outcomes.

BACKGROUND: Cardiomyopathy causes more than a third of late postpartum pregnancy-related deaths in the United States, and racial disparities in outcomes among pregnant individuals with cardiomyopathy exist. Underlying community factors may contribute to disparities in peripartum cardiomyopathy outcomes. OBJECTIVE: This study aimed to identify the geographic distribution of and disparities in peripartum cardiomyopathy outcomes, hypothesizing that patients living in communities with higher social vulnerability may have worse outcomes. STUDY DESIGN: This was a retrospective cohort study of patients with peripartum cardiomyopathy per the National Heart, Lung, and Blood Institute definition from January 2000 to November 2017 at a single center, excluding those with a post office box address as a post office box address may not reflect the census tract in which a patient resides. Severe peripartum cardiomyopathy (vs less severe peripartum cardiomyopathy) was defined as ejection fraction <30%, death, intensive care unit admission, left ventricular assist device or implantable cardioverter defibrillator placement, or transplant. The US census tract for the patient's address was linked to the Centers for Disease Control and Prevention Social Vulnerability Index, a 0 to 1 scale of a community's vulnerability to external stresses on health, with higher values indicating greater vulnerability. The Social Vulnerability Index includes social factors divided into socioeconomic, household composition, minority status, and housing type and transportation themes. The Social Vulnerability Index and Social Vulnerability Index components were compared among patients by peripartum cardiomyopathy severity. RESULTS: Of 95 patients in the original cohort, 5 were excluded because of the use of a post office box address. Of the remaining 90 patients, 56 met severe peripartum cardiomyopathy criteria. At baseline, individuals with and without severe peripartum cardiomyopathy had similar ages, marital status, payor type, tobacco use, gestational age at delivery, and mode of delivery; however, individuals with severe peripartum cardiomyopathy were more likely to be Black (vs White) (59% vs 29%; P<.007) and less likely to recover ejection fraction (EF) to ≥55% by 12 months (36% vs 62%; P=.02) than individuals with less severe peripartum cardiomyopathy. Patients with severe peripartum cardiomyopathy were more likely to live in areas with a higher Social Vulnerability Index (0.51 vs 0.31; P=.002) and with more residents who were unemployed, impoverished, without a high school diploma, in single-parent households, of minority status, without a vehicle, and in institutionalized group quarters than patients with less severe peripartum cardiomyopathy. The median income was lower in communities of individuals with severe peripartum cardiomyopathy than in communities of individuals with less severe peripartum cardiomyopathy. CONCLUSION: Patients with severe peripartum cardiomyopathy outcomes were more likely to live in communities with greater social vulnerability than patients with less severe peripartum cardiomyopathy outcomes. To reduce disparities and maternal mortality rates, resources may need to be directed to socially vulnerable communities.

Characterizing the neighborhood risk environment in multisite clinic-based cohort studies: A practical geocoding and data linkages protocol for protected health information.

BACKGROUND: Maintaining patient privacy when geocoding and linking residential address information with neighborhood-level data can create challenges during research. Challenges may arise when study staff have limited training in geocoding and linking data, or when non-study staff with appropriate expertise have limited availability, are unfamiliar with a study's population or objectives, or are not affordable for the study team. Opportunities for data breaches may also arise when working with non-study staff who are not on-site. We detail a free, user-friendly protocol for constructing indices of the neighborhood risk environment during multisite, clinic-based cohort studies that rely on participants' protected health information. This protocol can be implemented by study staff who do not have prior training in Geographic Information Systems (GIS) and can help minimize the operational costs of integrating geographic data into public health projects. METHODS: This protocol demonstrates how to: (1) securely geocode patients' residential addresses in a clinic setting and match geocoded addresses to census tracts using Geographic Information System software (Esri, Redlands, CA); (2) ascertain contextual variables of the risk environment from the American Community Survey and ArcGIS Business Analyst (Esri, Redlands, CA); (3) use geoidentifiers to link neighborhood risk data to census tracts containing geocoded addresses; and (4) assign randomly generated identifiers to census tracts and strip census tracts of their geoidentifiers to maintain patient confidentiality. RESULTS: Completion of this protocol generates three neighborhood risk indices (i.e., Neighborhood Disadvantage Index, Murder Rate Index, and Assault Rate Index) for patients' coded census tract locations. CONCLUSIONS: This protocol can be used by research personnel without prior GIS experience to easily create objective indices of the neighborhood risk environment while upholding patient confidentiality. Future studies can adapt this protocol to fit their specific patient populations and analytic objectives.

Examining the Built Environment for Healthy Living via Virtual Street Audits.

During the fall 2019 and spring 2020 semesters, 156 MPH students enrolled in the Integrative Learning Experience at the University of Alabama at Birmingham School of Public Health explored concepts of the built environment and health by auditing 2500 street segments in 4 urban neighborhoods in Birmingham, Alabama. In teams of 4 to 5, in-class and online students worked collaboratively to assess 63 built environment variables related to transportation, land use, advertisement, and neighborhood physical disorder. This type of "community assessment" is the first stage of the Evidence-based Public Health Framework and consistent with the applied nature of an MPH degree. Authors conducted secondary data analysis of final team assignments to demonstrate how students translated observations and ratings into practical recommendations for neighborhood improvements to promote physical activity. Students recommended improvements in neighborhood infrastructure and services, specifically: creating exercise space, providing outdoor exercise equipment, improving neighborhood safety, and cultivating a culture of health. The Integrative Learning Experience course encouraged students to use their knowledge and skills to prioritize recommendations to improve neighborhood conditions. Variable ratings and observations increased student awareness of the built environment and its potential to impact individual and community health. Moreover, the project helped students make connections between proximal outcomes, such as improving neighborhood walkability, and distal outcomes, such as improved health outcomes among residents. Finally, this project modeled for students the use of evidence-based strategies for making data-informed decisions, which are essential skills for new and emerging public health professionals.

County-level Differences in Liver-related Mortality, Waitlisting, and Liver Transplantation in the United States.

BACKGROUND: Much of our understanding regarding geographic issues in transplantation is based on statistical techniques that do not formally account for geography and is based on obsolete boundaries such as donation service area. METHODS: We applied spatial epidemiological techniques to analyze liver-related mortality and access to liver transplant services at the county level using data from the Centers for Disease Control and Prevention and Scientific Registry of Transplant Recipients from 2010 to 2018. RESULTS: There was a significant negative spatial correlation between transplant rates and liver-related mortality at the county level (Moran's I, -0.319; P = 0.001). Significant clusters were identified with high transplant rates and low liver-related mortality. Counties in geographic clusters with high ratios of liver transplants to liver-related deaths had more liver transplant centers within 150 nautical miles (6.7 versus 3.6 centers; P < 0.001) compared with all other counties, as did counties in geographic clusters with high ratios of waitlist additions to liver-related deaths (8.5 versus 2.5 centers; P < 0.001). The spatial correlation between waitlist mortality and overall liver-related mortality was positive (Moran's I, 0.060; P = 0.001) but weaker. Several areas with high waitlist mortality had some of the lowest overall liver-related mortality in the country. CONCLUSIONS: These data suggest that high waitlist mortality and allocation model for end-stage liver disease do not necessarily correlate with decreased access to transplant, whereas local transplant center density is associated with better access to waitlisting and transplant.

Lost potential and missed opportunities for DCD liver transplantation in the United States.

BACKGROUND: Donation after cardiac death(DCD) has been proposed as an avenue to expand the liver donor pool. METHODS: We examined factors associated with nonrecovery of DCD livers using UNOS data from 2015 to 2019. RESULTS: There 265 non-recovered potential(NRP) DCD livers. Blood type AB (7.8% vs. 1.1%) and B (16.9% vs. 9.8%) were more frequent in the NRP versus actual donors (p < 0.001). The median driving time between donor hospital and transplant center was similar for NRP and actual donors (30.1 min vs. 30.0 min; p = 0.689), as was the percentage located within a transplant hospital (20.8% vs. 20.9%; p = 0.984).The donation service area(DSA) of a donor hospital explained 27.9% (p = 0.001) of the variability in whether a DCD liver was recovered. CONCLUSION: A number of potentially high quality DCD donor livers go unrecovered each year, which may be partially explained by donor blood type and variation in regional and DSA level practice patterns.

Assessing Hispanic/Latino and Non-Hispanic White Social Determinants of Obesity Among a Community Sample of Residents in the Rural Southeast US.

Employing an ecological approach, we sought to identify social determinants of obesity among Hispanics/Latinos and non-Hispanic whites living in the Southeast US. Data on social determinants of obesity (individual, family, community and cultural/contextual) were collected from 217 participants [106 Hispanics/Latinos; 111 non-Hispanic whites]; height and weight  were objectively measured. We compared prevalence of overweight and obese between ethnic groups and BMI values within each group by social determinants. Hispanics had a 1.9-fold increase (OR 1.93, 95% CI: 1.05-3.55) in overweight prevalence compared to non-Hispanic whites after adjusting for age and gender. We found positive estimates between unfavorable family-level determinants and BMI among Hispanic/Latinos. In contrast, non-Hispanic whites who reported unfavorable neighborhood characteristics had higher BMI's. Findings highlight the need for targeted approaches for the prevention and control of obesity.

Area based stratified random sampling using geospatial technology in a community-based survey.

BACKGROUND: Most studies among Hispanics have focused on individual risk factors of obesity, with less attention on interpersonal, community and environmental determinants. Conducting community based surveys to study these determinants must ensure representativeness of disparate populations. We describe the use of a novel Geographic Information System (GIS)-based population based sampling to minimize selection bias in a rural community based study. METHODS: We conducted a community based survey to collect and examine social determinants of health and their association with obesity prevalence among a sample of Hispanics and non-Hispanic whites living in a rural community in the Southeastern United States. To ensure a balanced sample of both ethnic groups, we designed an area stratified random sampling procedure involving three stages: (1) division of the sampling area into non-overlapping strata based on Hispanic household proportion using GIS software; (2) random selection of the designated number of Census blocks from each stratum; and (3) random selection of the designated number of housing units (i.e., survey participants) from each Census block. RESULTS: The proposed sample included 109 Hispanic and 107 non-Hispanic participants to be recruited from 44 Census blocks. The final sample included 106 Hispanic and 111 non-Hispanic participants. The proportion of Hispanic surveys completed per strata matched our proposed distribution: 7% for strata 1, 30% for strata 2, 58% for strata 3 and 83% for strata 4. CONCLUSION: Utilizing a standardized area based randomized sampling approach allowed us to successfully recruit an ethnically balanced sample while conducting door to door surveys in a rural, community based study. The integration of area based randomized sampling using tools such as GIS in future community-based research should be considered, particularly when trying to reach disparate populations.

Life-Space Predicts Health Care Utilization in Community-Dwelling Older Adults.

OBJECTIVE: To determine whether decline in life-space mobility predicts increased health care utilization among community-dwelling older adults. METHOD: Health care utilization (number of emergency department [ED] visits and hospitalizations) was self-reported during monthly interviews among 419 community-dwelling African American and non-Hispanic White adults aged 75 years and older in The University of Alabama at Birmingham (UAB) Study of Aging II. Life-space was measured using the UAB Life-Space Assessment. Generalized estimating equations were used to examine associations of life-space at the beginning of each interval with health care utilization over the 1-month interval. RESULTS: Overall, 400 participants were followed for 36 months. A 10-point decrease in life-space was associated with 14% increased odds of an ED visit and/or hospitalization over the next month, adjusting for demographics, transportation difficulty, comorbidity, and having a doctor visit in the last month. DISCUSSION: Life-space is a practical alternative in predicting future health care utilization to performance-based measures, which can be difficult to incorporate into clinical or public health practice.

Reach of Effective, Nationally-Available, Low-Cost, Nonprofit Weight Loss Program in Medically Underserved Areas (MUAs).

Obesity rates are higher for ethnic minority, low-income, and rural communities. Programs are needed to support these communities with weight management. We determined the reach of a low-cost, nationally-available weight loss program in Health Resources and Services Administration medically underserved areas (MUAs) and described the demographics of the communities with program locations. This is a cross-sectional analysis of Take Off Pounds Sensibly (TOPS) chapter locations. Geographic information systems technology was used to combine information about TOPS chapter locations, the geographic boundaries of MUAs, and socioeconomic data from the Decennial 2010 Census. TOPS is available in 30 % of MUAs. The typical TOPS chapter is in a Census Tract that is predominantly white, urban, with a median annual income between $25,000 and $50,000. However, there are TOPS chapters in Census Tracts that can be classified as predominantly black or predominantly Hispanic; predominantly rural; and as low or high income. TOPS provides weight management services in MUAs and across many types of communities. TOPS can help treat obesity in the medically underserved. Future research should determine the differential effectiveness among chapters in different types of communities.

Multiple cluster analysis for the identification of high-risk census tracts for out-of-hospital cardiac arrest (OHCA) in Denver, Colorado.

BACKGROUND: Prior research has shown that high-risk census tracts for out-of-hospital cardiac arrest (OHCA) can be identified. High-risk neighborhoods are defined as having a high incidence of OHCA and a low prevalence of bystander cardiopulmonary resuscitation (CPR). However, there is no consensus regarding the process for identifying high-risk neighborhoods. OBJECTIVE: We propose a novel summary approach to identify high-risk neighborhoods through three separate spatial analysis methods: Empirical Bayes (EB), Local Moran's I (LISA), and Getis Ord Gi* (Gi*) in Denver, Colorado. METHODS: We conducted a secondary analysis of prospectively collected Emergency Medical Services data of OHCA from January 1, 2009 to December 31, 2011 from the City and County of Denver, Colorado. OHCA incidents were restricted to those of cardiac etiology in adults ≥18 years. The OHCA incident locations were geocoded using Centrus. EB smoothed incidence rates were calculated for OHCA using Geoda and LISA and Gi* calculated using ArcGIS 10. RESULTS: A total of 1102 arrests in 142 census tracts occurred during the study period, with 887 arrests included in the final sample. Maps of clusters of high OHCA incidence were overlaid with maps identifying census tracts in the below the Denver County mean for bystander CPR prevalence. Five census tracts identified were designated as Tier 1 high-risk tracts, while an additional 7 census tracts where designated as Tier 2 high-risk tracts. CONCLUSION: This is the first study to use these three spatial cluster analysis methods for the detection of high-risk census tracts. These census tracts are possible sites for targeted community-based interventions to improve both cardiovascular health education and CPR training.

A geospatial mixed methods approach to assessing campus safety.

BACKGROUND: While there is no panacea for alleviating campus safety concerns, safety experts agree that one of the key components to an effective campus security plan is monitoring the environment. Despite previous attempts to measure campus safety, quantifying perceptions of fear, safety, and risk remains a challenging issue. Since perceptions of safety and incidents of crime do not necessarily mirror one another, both were utilized in this investigation. PURPOSE: The purpose of this article is to describe an innovative, mixed methods approach for assessing campus safety at a large, urban campus in the southeast region of the United States. METHOD: A concurrent triangulation design was implemented to allow investigators the opportunity to collect qualitative and quantitative data simultaneously and integrate results in the interpretation phase. Data were collected from four distinct sources of information. RESULTS: Student focus groups yielded data regarding perceptions of risk, and kernel density analysis was used to identify "hot spots" of campus crime incidents. CONCLUSION: While in many cases perceived risk and actual crime incidents were associated, incidents of hot spots of each type occurred independently with such frequency that an overall correlation of the two was not significant. Accordingly, while no significant correlation between perceived risk and crime incidents was confirmed statistically, the geospatial integration of these data suggested three types of safety conditions. Further, the combination of focus group data and spatial analyses provided a more comprehensive and, therefore, more complete understanding of the multifaceted issues related to campus safety.

Identifying high-risk geographic areas for cardiac arrest using three methods for cluster analysis.

OBJECTIVES: The objective was to identify high-risk census tracts, defined as those areas that have both a high incidence of out-of-hospital cardiac arrest (OHCA) and a low prevalence of bystander cardiopulmonary resuscitation (CPR), by using three spatial statistical methods. METHODS: This was a secondary analysis of two prospectively collected registries in the city of Columbus, Ohio. Consecutive adult (≥18 years) OHCA patients, restricted to those of cardiac etiology and treated by emergency medical services (EMS) from April 1, 2004, to April 30, 2009, were studied. Three different spatial analysis methods (Global Empirical Bayes, Local Moran's I, and SaTScan's spatial scan statistic) were used to identify high-risk census tracts. RESULTS: A total of 4,553 arrests in 200 census tracts occurred during the study period, with 1,632 arrests included in the final sample after exclusions for no resuscitation attempt, noncardiac etiology, etc. The overall incidence for OHCA was 0.70 per 1,000 people for the 6-year study period (SD = ±0.52). Bystander CPR occurred in 20.2% (n = 329), with 10.0% (n = 167) surviving to hospital discharge. Five high-risk census tracts were identified by all three analytic methods. CONCLUSIONS: The five high-risk census tracts identified may be possible sites for high-yield targeted community-based interventions to improve CPR training and cardiovascular disease education efforts and ultimately improve survival from OHCA.